Machine Learning Methods in Drug Discovery.

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Lauv PatelShanzhi Wang

Abstract

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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Citations

May 1, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Mantas VaškevičiusLiudas Šlepikas
Jun 18, 2021·Cell Systems·Yuge JiFabian J Theis
Jul 15, 2021·BioMed Research International·Arun Bahadur GurungKhalid Mashay Al-Anazi
Jul 31, 2021·Journal of Healthcare Engineering·Nishant JhaM Saravana Kumar
Aug 6, 2021·Molecular Diversity·Samuel K KwofieMichael D Wilson
Nov 2, 2021·Frontiers in Cellular and Infection Microbiology·Dingchen LiLi Han

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Methods Mentioned

BETA
interaction prediction

Software Mentioned

GeneCluster
ML
QSAR

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